Overview

Dataset statistics

Number of variables17
Number of observations13406
Missing cells158110
Missing cells (%)69.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory136.0 B

Variable types

Categorical1
Numeric16

Alerts

Country Name has a high cardinality: 217 distinct values High cardinality
CPI human resources is highly correlated with CPIA gender equality and 1 other fieldsHigh correlation
CPIA gender equality is highly correlated with CPI human resources and 5 other fieldsHigh correlation
CPIA social protection is highly correlated with CPI human resources and 2 other fieldsHigh correlation
fertility rate is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
intentional homicides is highly correlated with poverty gap and 1 other fieldsHigh correlation
literacy rate is highly correlated with CPIA gender equality and 4 other fieldsHigh correlation
poverty gap is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
primary completion is highly correlated with CPIA gender equality and 4 other fieldsHigh correlation
unpaid domestic is highly correlated with CPIA social protection and 1 other fieldsHigh correlation
teenage mothers is highly correlated with fertility rate and 5 other fieldsHigh correlation
married by 18 is highly correlated with fertility rate and 4 other fieldsHigh correlation
CPI human resources is highly correlated with CPIA gender equality and 1 other fieldsHigh correlation
CPIA gender equality is highly correlated with CPI human resources and 5 other fieldsHigh correlation
CPIA social protection is highly correlated with CPI human resources and 2 other fieldsHigh correlation
fertility rate is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
intentional homicides is highly correlated with teenage mothersHigh correlation
literacy rate is highly correlated with CPIA gender equality and 4 other fieldsHigh correlation
poverty gap is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
primary completion is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
unpaid domestic is highly correlated with CPIA social protectionHigh correlation
teenage mothers is highly correlated with fertility rate and 4 other fieldsHigh correlation
married by 18 is highly correlated with fertility rate and 4 other fieldsHigh correlation
CPI human resources is highly correlated with CPIA gender equality and 1 other fieldsHigh correlation
CPIA gender equality is highly correlated with CPI human resourcesHigh correlation
CPIA social protection is highly correlated with CPI human resourcesHigh correlation
fertility rate is highly correlated with literacy rate and 2 other fieldsHigh correlation
literacy rate is highly correlated with fertility rate and 2 other fieldsHigh correlation
poverty gap is highly correlated with fertility rate and 1 other fieldsHigh correlation
primary completion is highly correlated with fertility rate and 1 other fieldsHigh correlation
teenage mothers is highly correlated with married by 18High correlation
married by 18 is highly correlated with teenage mothersHigh correlation
CPI human resources is highly correlated with CPIA gender equality and 3 other fieldsHigh correlation
CPIA gender equality is highly correlated with CPI human resources and 7 other fieldsHigh correlation
CPIA social protection is highly correlated with CPI human resources and 2 other fieldsHigh correlation
management is highly correlated with CPIA gender equality and 3 other fieldsHigh correlation
fertility rate is highly correlated with CPIA gender equality and 6 other fieldsHigh correlation
intentional homicides is highly correlated with teenage mothersHigh correlation
labor force is highly correlated with CPIA gender equality and 7 other fieldsHigh correlation
literacy rate is highly correlated with CPIA gender equality and 6 other fieldsHigh correlation
poverty gap is highly correlated with CPIA gender equality and 7 other fieldsHigh correlation
primary completion is highly correlated with CPI human resources and 8 other fieldsHigh correlation
parliment seats is highly correlated with teenage mothersHigh correlation
unpaid domestic is highly correlated with CPI human resources and 6 other fieldsHigh correlation
teenage mothers is highly correlated with management and 8 other fieldsHigh correlation
married by 18 is highly correlated with fertility rate and 5 other fieldsHigh correlation
CPI human resources has 12189 (90.9%) missing values Missing
CPIA gender equality has 12189 (90.9%) missing values Missing
CPIA social protection has 12197 (91.0%) missing values Missing
employers has 7983 (59.5%) missing values Missing
management has 12391 (92.4%) missing values Missing
fertility rate has 1458 (10.9%) missing values Missing
intentional homicides has 11330 (84.5%) missing values Missing
labor force has 7422 (55.4%) missing values Missing
literacy rate has 12446 (92.8%) missing values Missing
poverty gap has 11591 (86.5%) missing values Missing
primary completion has 8881 (66.2%) missing values Missing
parliment seats has 8887 (66.3%) missing values Missing
unpaid domestic has 13229 (98.7%) missing values Missing
teenage mothers has 13039 (97.3%) missing values Missing
married by 18 has 12878 (96.1%) missing values Missing
Country Name is uniformly distributed Uniform
parliment seats has 181 (1.4%) zeros Zeros

Reproduction

Analysis started2022-05-18 05:20:04.569751
Analysis finished2022-05-18 05:20:42.515261
Duration37.95 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct217
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size104.9 KiB
Afghanistan
 
62
Myanmar
 
62
Nauru
 
62
Nepal
 
62
Netherlands
 
62
Other values (212)
13096 

Length

Max length30
Median length22
Mean length9.643368641
Min length4

Characters and Unicode

Total characters129279
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan62
 
0.5%
Myanmar62
 
0.5%
Nauru62
 
0.5%
Nepal62
 
0.5%
Netherlands62
 
0.5%
New Caledonia62
 
0.5%
New Zealand62
 
0.5%
Nicaragua62
 
0.5%
Niger62
 
0.5%
Nigeria62
 
0.5%
Other values (207)12786
95.4%

Length

2022-05-18T01:20:42.625225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
islands556
 
2.9%
and496
 
2.5%
rep434
 
2.2%
republic372
 
1.9%
st225
 
1.2%
china186
 
1.0%
arab186
 
1.0%
the186
 
1.0%
new186
 
1.0%
guinea186
 
1.0%
Other values (255)16457
84.5%

Most occurring characters

ValueCountFrequency (%)
a18360
 
14.2%
i10020
 
7.8%
n9991
 
7.7%
e8900
 
6.9%
r7118
 
5.5%
o6382
 
4.9%
6064
 
4.7%
s4624
 
3.6%
u4588
 
3.5%
l4584
 
3.5%
Other values (48)48648
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter101434
78.5%
Uppercase Letter19370
 
15.0%
Space Separator6064
 
4.7%
Other Punctuation1961
 
1.5%
Open Punctuation163
 
0.1%
Close Punctuation163
 
0.1%
Dash Punctuation124
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a18360
18.1%
i10020
9.9%
n9991
9.8%
e8900
 
8.8%
r7118
 
7.0%
o6382
 
6.3%
s4624
 
4.6%
u4588
 
4.5%
l4584
 
4.5%
t4518
 
4.5%
Other values (16)22349
22.0%
Uppercase Letter
ValueCountFrequency (%)
S2147
 
11.1%
C1550
 
8.0%
M1525
 
7.9%
B1426
 
7.4%
R1364
 
7.0%
A1302
 
6.7%
I1237
 
6.4%
G1115
 
5.8%
T992
 
5.1%
P868
 
4.5%
Other values (15)5844
30.2%
Other Punctuation
ValueCountFrequency (%)
.1031
52.6%
,806
41.1%
'124
 
6.3%
Space Separator
ValueCountFrequency (%)
6064
100.0%
Open Punctuation
ValueCountFrequency (%)
(163
100.0%
Close Punctuation
ValueCountFrequency (%)
)163
100.0%
Dash Punctuation
ValueCountFrequency (%)
-124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin120804
93.4%
Common8475
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a18360
15.2%
i10020
 
8.3%
n9991
 
8.3%
e8900
 
7.4%
r7118
 
5.9%
o6382
 
5.3%
s4624
 
3.8%
u4588
 
3.8%
l4584
 
3.8%
t4518
 
3.7%
Other values (41)41719
34.5%
Common
ValueCountFrequency (%)
6064
71.6%
.1031
 
12.2%
,806
 
9.5%
(163
 
1.9%
)163
 
1.9%
-124
 
1.5%
'124
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII129279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a18360
 
14.2%
i10020
 
7.8%
n9991
 
7.7%
e8900
 
6.9%
r7118
 
5.5%
o6382
 
4.9%
6064
 
4.7%
s4624
 
3.6%
u4588
 
3.5%
l4584
 
3.5%
Other values (48)48648
37.6%

Year
Real number (ℝ≥0)

Distinct62
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1990.553558
Minimum1960
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:42.769694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1960
5-th percentile1963
Q11975
median1991
Q32006
95-th percentile2018
Maximum2021
Range61
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.87826764
Coefficient of variation (CV)0.008981555694
Kurtosis-1.198413465
Mean1990.553558
Median Absolute Deviation (MAD)15
Skewness-0.00486870527
Sum26685361
Variance319.632454
MonotonicityNot monotonic
2022-05-18T01:20:42.910581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1991217
 
1.6%
1990217
 
1.6%
1983217
 
1.6%
1984217
 
1.6%
1985217
 
1.6%
1986217
 
1.6%
1987217
 
1.6%
1988217
 
1.6%
1989217
 
1.6%
2001217
 
1.6%
Other values (52)11236
83.8%
ValueCountFrequency (%)
1960215
1.6%
1961215
1.6%
1962215
1.6%
1963215
1.6%
1964215
1.6%
1965215
1.6%
1966215
1.6%
1967215
1.6%
1968215
1.6%
1969215
1.6%
ValueCountFrequency (%)
2021212
1.6%
2020217
1.6%
2019217
1.6%
2018217
1.6%
2017217
1.6%
2016217
1.6%
2015217
1.6%
2014217
1.6%
2013217
1.6%
2012217
1.6%

CPI human resources
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.7%
Missing12189
Missing (%)90.9%
Infinite0
Infinite (%)0.0%
Mean3.523418242
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:43.031231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q13
median3.5
Q34
95-th percentile4.5
Maximum4.5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6107470755
Coefficient of variation (CV)0.1733393635
Kurtosis-0.03020094607
Mean3.523418242
Median Absolute Deviation (MAD)0.5
Skewness-0.4312014692
Sum4288
Variance0.3730119902
MonotonicityNot monotonic
2022-05-18T01:20:43.132929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.5380
 
2.8%
4338
 
2.5%
3226
 
1.7%
4.5130
 
1.0%
2.5120
 
0.9%
219
 
0.1%
1.52
 
< 0.1%
12
 
< 0.1%
(Missing)12189
90.9%
ValueCountFrequency (%)
12
 
< 0.1%
1.52
 
< 0.1%
219
 
0.1%
2.5120
 
0.9%
3226
1.7%
3.5380
2.8%
4338
2.5%
4.5130
 
1.0%
ValueCountFrequency (%)
4.5130
 
1.0%
4338
2.5%
3.5380
2.8%
3226
1.7%
2.5120
 
0.9%
219
 
0.1%
1.52
 
< 0.1%
12
 
< 0.1%

CPIA gender equality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.7%
Missing12189
Missing (%)90.9%
Infinite0
Infinite (%)0.0%
Mean3.352095316
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:43.239303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.5
Q13
median3.5
Q34
95-th percentile4.5
Maximum5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6644628055
Coefficient of variation (CV)0.198223124
Kurtosis-0.1290391282
Mean3.352095316
Median Absolute Deviation (MAD)0.5
Skewness-0.2024222719
Sum4079.5
Variance0.4415108199
MonotonicityNot monotonic
2022-05-18T01:20:43.911091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.5335
 
2.5%
3310
 
2.3%
4261
 
1.9%
2.5164
 
1.2%
4.583
 
0.6%
237
 
0.3%
1.517
 
0.1%
510
 
0.1%
(Missing)12189
90.9%
ValueCountFrequency (%)
1.517
 
0.1%
237
 
0.3%
2.5164
1.2%
3310
2.3%
3.5335
2.5%
4261
1.9%
4.583
 
0.6%
510
 
0.1%
ValueCountFrequency (%)
510
 
0.1%
4.583
 
0.6%
4261
1.9%
3.5335
2.5%
3310
2.3%
2.5164
1.2%
237
 
0.3%
1.517
 
0.1%

CPIA social protection
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.7%
Missing12197
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean3.043424318
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:44.016606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median3
Q33.5
95-th percentile4
Maximum4.5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5832727823
Coefficient of variation (CV)0.1916501682
Kurtosis0.2833720957
Mean3.043424318
Median Absolute Deviation (MAD)0.5
Skewness-0.3228528381
Sum3679.5
Variance0.3402071385
MonotonicityNot monotonic
2022-05-18T01:20:44.122968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.5408
 
3.0%
3339
 
2.5%
2.5273
 
2.0%
287
 
0.6%
470
 
0.5%
4.518
 
0.1%
18
 
0.1%
1.56
 
< 0.1%
(Missing)12197
91.0%
ValueCountFrequency (%)
18
 
0.1%
1.56
 
< 0.1%
287
 
0.6%
2.5273
2.0%
3339
2.5%
3.5408
3.0%
470
 
0.5%
4.518
 
0.1%
ValueCountFrequency (%)
4.518
 
0.1%
470
 
0.5%
3.5408
3.0%
3339
2.5%
2.5273
2.0%
287
 
0.6%
1.56
 
< 0.1%
18
 
0.1%

employers
Real number (ℝ≥0)

MISSING

Distinct628
Distinct (%)11.6%
Missing7983
Missing (%)59.5%
Infinite0
Infinite (%)0.0%
Mean1.740768947
Minimum0
Maximum10.86999989
Zeros58
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:44.258343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1400000006
Q10.6000000238
median1.450000048
Q32.425000072
95-th percentile4.420000076
Maximum10.86999989
Range10.86999989
Interquartile range (IQR)1.825000048

Descriptive statistics

Standard deviation1.518537384
Coefficient of variation (CV)0.8723371285
Kurtosis5.763855435
Mean1.740768947
Median Absolute Deviation (MAD)0.8999998569
Skewness1.88282576
Sum9440.189998
Variance2.305955788
MonotonicityNot monotonic
2022-05-18T01:20:44.404900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058
 
0.4%
0.15000000651
 
0.4%
0.479999989344
 
0.3%
0.330000013143
 
0.3%
0.469999998840
 
0.3%
0.119999997340
 
0.3%
0.140000000639
 
0.3%
0.0500000007535
 
0.3%
0.533
 
0.2%
0.959999978532
 
0.2%
Other values (618)5008
37.4%
(Missing)7983
59.5%
ValueCountFrequency (%)
058
0.4%
0.0099999997765
 
< 0.1%
0.019999999554
 
< 0.1%
0.029999999336
 
< 0.1%
0.0399999991129
0.2%
0.0500000007535
0.3%
0.059999998669
 
0.1%
0.07000000034
 
< 0.1%
0.0799999982111
 
0.1%
0.090000003588
 
0.1%
ValueCountFrequency (%)
10.869999891
< 0.1%
10.819999691
< 0.1%
10.770000461
< 0.1%
10.710000041
< 0.1%
10.479999541
< 0.1%
10.470000271
< 0.1%
10.319999691
< 0.1%
10.260000231
< 0.1%
10.251
< 0.1%
10.229999541
< 0.1%

management
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct868
Distinct (%)85.5%
Missing12391
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean30.7026601
Minimum4.21999979
Maximum60.90999985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:44.545008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.21999979
5-th percentile15.48800001
Q124.75
median31.45999908
Q336.625
95-th percentile43.23599968
Maximum60.90999985
Range56.69000006
Interquartile range (IQR)11.875

Descriptive statistics

Standard deviation8.761744902
Coefficient of variation (CV)0.2853741296
Kurtosis0.2124899072
Mean30.7026601
Median Absolute Deviation (MAD)5.810001373
Skewness-0.2350916434
Sum31163.2
Variance76.76817372
MonotonicityNot monotonic
2022-05-18T01:20:44.685490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.53
 
< 0.1%
30.540000923
 
< 0.1%
34.259998323
 
< 0.1%
34.340000153
 
< 0.1%
35.580001833
 
< 0.1%
40.259998323
 
< 0.1%
33.319999693
 
< 0.1%
33.139999393
 
< 0.1%
23.110000613
 
< 0.1%
30.819999693
 
< 0.1%
Other values (858)985
 
7.3%
(Missing)12391
92.4%
ValueCountFrequency (%)
4.219999791
< 0.1%
4.2600002291
< 0.1%
4.51
< 0.1%
5.2300000191
< 0.1%
5.6999998091
< 0.1%
5.7300000191
< 0.1%
5.751
< 0.1%
6.0799999241
< 0.1%
6.4000000951
< 0.1%
7.6199998861
< 0.1%
ValueCountFrequency (%)
60.909999851
< 0.1%
59.310001371
< 0.1%
57.970001221
< 0.1%
55.830001831
< 0.1%
54.639999391
< 0.1%
54.169998171
< 0.1%
53.680000311
< 0.1%
51.830001831
< 0.1%
50.490001681
< 0.1%
50.189998631
< 0.1%

fertility rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5024
Distinct (%)42.0%
Missing1458
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean3.954937889
Minimum0.837
Maximum8.864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:44.831908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.837
5-th percentile1.43
Q12.09
median3.509
Q35.866
95-th percentile7.2233
Maximum8.864
Range8.027
Interquartile range (IQR)3.776

Descriptive statistics

Standard deviation2.012936501
Coefficient of variation (CV)0.5089679175
Kurtosis-1.281893526
Mean3.954937889
Median Absolute Deviation (MAD)1.665
Skewness0.3396013253
Sum47253.5979
Variance4.051913358
MonotonicityNot monotonic
2022-05-18T01:20:44.977277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.634
 
0.3%
1.7533
 
0.2%
1.7132
 
0.2%
1.532
 
0.2%
1.7430
 
0.2%
1.4630
 
0.2%
1.929
 
0.2%
1.429
 
0.2%
2.0628
 
0.2%
1.5228
 
0.2%
Other values (5014)11643
86.8%
(Missing)1458
 
10.9%
ValueCountFrequency (%)
0.8371
< 0.1%
0.861
< 0.1%
0.8621
< 0.1%
0.8681
< 0.1%
0.8751
< 0.1%
0.8791
< 0.1%
0.91
< 0.1%
0.9011
< 0.1%
0.9061
< 0.1%
0.9111
< 0.1%
ValueCountFrequency (%)
8.8641
< 0.1%
8.8581
< 0.1%
8.8531
< 0.1%
8.8331
< 0.1%
8.8281
< 0.1%
8.7931
< 0.1%
8.7861
< 0.1%
8.7521
< 0.1%
8.7131
< 0.1%
8.711
< 0.1%

intentional homicides
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1955
Distinct (%)94.2%
Missing11330
Missing (%)84.5%
Infinite0
Infinite (%)0.0%
Mean2.34666783
Minimum0
Maximum19.17122899
Zeros121
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:45.132079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.704246345
median1.339727909
Q33.062671185
95-th percentile8.134190226
Maximum19.17122899
Range19.17122899
Interquartile range (IQR)2.35842484

Descriptive statistics

Standard deviation2.624446621
Coefficient of variation (CV)1.118371585
Kurtosis6.550194652
Mean2.34666783
Median Absolute Deviation (MAD)0.8319602577
Skewness2.318405185
Sum4871.682414
Variance6.887720065
MonotonicityNot monotonic
2022-05-18T01:20:45.281363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0121
 
0.9%
10.52
 
< 0.1%
0.52231503951
 
< 0.1%
0.95679569121
 
< 0.1%
2.0912189721
 
< 0.1%
2.1135950011
 
< 0.1%
2.1117003841
 
< 0.1%
1.8433742081
 
< 0.1%
2.6396135611
 
< 0.1%
3.2413601161
 
< 0.1%
Other values (1945)1945
 
14.5%
(Missing)11330
84.5%
ValueCountFrequency (%)
0121
0.9%
0.064867613851
 
< 0.1%
0.084343131471
 
< 0.1%
0.1125425271
 
< 0.1%
0.12175062791
 
< 0.1%
0.12214839481
 
< 0.1%
0.12518981911
 
< 0.1%
0.13141903391
 
< 0.1%
0.14141065181
 
< 0.1%
0.15312736481
 
< 0.1%
ValueCountFrequency (%)
19.171228991
< 0.1%
18.276901851
< 0.1%
17.253034481
< 0.1%
17.158123511
< 0.1%
16.603573091
< 0.1%
15.546899271
< 0.1%
14.488466581
< 0.1%
14.343405141
< 0.1%
14.056248231
< 0.1%
13.9986141
< 0.1%

labor force
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5655
Distinct (%)94.5%
Missing7422
Missing (%)55.4%
Infinite0
Infinite (%)0.0%
Mean50.33109058
Minimum5.994999886
Maximum90.55500031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:45.434283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.994999886
5-th percentile18.03975067
Q141.10674858
median51.06199837
Q360.75949955
95-th percentile77.92239685
Maximum90.55500031
Range84.56000042
Interquartile range (IQR)19.65275097

Descriptive statistics

Standard deviation16.48750625
Coefficient of variation (CV)0.3275809458
Kurtosis-0.1074837848
Mean50.33109058
Median Absolute Deviation (MAD)9.769001007
Skewness-0.2389285789
Sum301181.246
Variance271.8378624
MonotonicityNot monotonic
2022-05-18T01:20:45.572511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.777999883
 
< 0.1%
58.937000273
 
< 0.1%
45.963001253
 
< 0.1%
50.167999273
 
< 0.1%
48.46200183
 
< 0.1%
50.081001283
 
< 0.1%
55.291999823
 
< 0.1%
49.333000183
 
< 0.1%
51.363998413
 
< 0.1%
59.721000673
 
< 0.1%
Other values (5645)5954
44.4%
(Missing)7422
55.4%
ValueCountFrequency (%)
5.9949998861
< 0.1%
6.0809998511
< 0.1%
6.0890002251
< 0.1%
6.094999792
< 0.1%
6.1149997711
< 0.1%
6.1290001871
< 0.1%
6.1380000111
< 0.1%
6.9580001831
< 0.1%
7.8819999691
< 0.1%
8.2819995881
< 0.1%
ValueCountFrequency (%)
90.555000311
< 0.1%
90.074996951
< 0.1%
89.572998051
< 0.1%
89.04900361
< 0.1%
88.50199891
< 0.1%
87.930999761
< 0.1%
87.811996461
< 0.1%
87.669998171
< 0.1%
87.527000431
< 0.1%
87.384002691
< 0.1%

literacy rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct943
Distinct (%)98.2%
Missing12446
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean76.73631724
Minimum3.390589952
Maximum99.99994659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:45.715205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.390589952
5-th percentile22.19071379
Q163.38534641
median88.74872208
Q394.93854332
95-th percentile99.51512718
Maximum99.99994659
Range96.60935664
Interquartile range (IQR)31.55319691

Descriptive statistics

Standard deviation24.87419081
Coefficient of variation (CV)0.3241514801
Kurtosis0.3053749178
Mean76.73631724
Median Absolute Deviation (MAD)9.468021393
Skewness-1.195081279
Sum73666.86455
Variance618.7253686
MonotonicityNot monotonic
2022-05-18T01:20:45.865309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.599998477
 
0.1%
98.251983644
 
< 0.1%
98.599998474
 
< 0.1%
693
 
< 0.1%
99.419998173
 
< 0.1%
99.738990782
 
< 0.1%
50.917739871
 
< 0.1%
57.900001531
 
< 0.1%
75.852981571
 
< 0.1%
88.558708191
 
< 0.1%
Other values (933)933
 
7.0%
(Missing)12446
92.8%
ValueCountFrequency (%)
3.3905899521
< 0.1%
3.6871700291
< 0.1%
4.5918297771
< 0.1%
4.9874601361
< 0.1%
5.7352800371
< 0.1%
8.0579795841
< 0.1%
8.2254295351
< 0.1%
8.3995800021
< 0.1%
8.5882596971
< 0.1%
9.1531200411
< 0.1%
ValueCountFrequency (%)
99.999946591
< 0.1%
99.9976121
< 0.1%
99.99587251
< 0.1%
99.985870361
< 0.1%
99.978408811
< 0.1%
99.97599031
< 0.1%
99.975769041
< 0.1%
99.958198551
< 0.1%
99.909477231
< 0.1%
99.907920841
< 0.1%

poverty gap
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct516
Distinct (%)28.4%
Missing11591
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean15.45415978
Minimum0
Maximum100
Zeros57
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:46.014212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median6.9
Q323.45
95-th percentile60.46
Maximum100
Range100
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation19.54230909
Coefficient of variation (CV)1.26453391
Kurtosis1.178607091
Mean15.45415978
Median Absolute Deviation (MAD)6.7
Skewness1.434647373
Sum28049.3
Variance381.9018446
MonotonicityNot monotonic
2022-05-18T01:20:46.161606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1142
 
1.1%
0.263
 
0.5%
057
 
0.4%
0.353
 
0.4%
0.744
 
0.3%
0.442
 
0.3%
0.542
 
0.3%
0.938
 
0.3%
0.629
 
0.2%
1.128
 
0.2%
Other values (506)1277
 
9.5%
(Missing)11591
86.5%
ValueCountFrequency (%)
057
0.4%
0.1142
1.1%
0.263
0.5%
0.353
 
0.4%
0.442
 
0.3%
0.542
 
0.3%
0.629
 
0.2%
0.744
 
0.3%
0.826
 
0.2%
0.938
 
0.3%
ValueCountFrequency (%)
1001
< 0.1%
86.61
< 0.1%
811
< 0.1%
79.71
< 0.1%
78.91
< 0.1%
78.51
< 0.1%
78.11
< 0.1%
77.81
< 0.1%
77.31
< 0.1%
76.61
< 0.1%

primary completion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4517
Distinct (%)99.8%
Missing8881
Missing (%)66.2%
Infinite0
Infinite (%)0.0%
Mean79.22523256
Minimum0.7209900022
Maximum142.1227264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:46.304002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.7209900022
5-th percentile19.75351143
Q162.51818848
median91.91253662
Q399.18698883
95-th percentile107.5226959
Maximum142.1227264
Range141.4017364
Interquartile range (IQR)36.66880035

Descriptive statistics

Standard deviation27.99984718
Coefficient of variation (CV)0.3534208267
Kurtosis0.01491142316
Mean79.22523256
Median Absolute Deviation (MAD)11.10713959
Skewness-1.026907218
Sum358494.1773
Variance783.9914424
MonotonicityNot monotonic
2022-05-18T01:20:46.441194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.578376772
 
< 0.1%
97.434158332
 
< 0.1%
96.330276492
 
< 0.1%
1002
 
< 0.1%
98.68614962
 
< 0.1%
102.06490332
 
< 0.1%
97.638046262
 
< 0.1%
7.0709400182
 
< 0.1%
95.226142881
 
< 0.1%
103.84615331
 
< 0.1%
Other values (4507)4507
33.6%
(Missing)8881
66.2%
ValueCountFrequency (%)
0.72099000221
< 0.1%
0.77954000231
< 0.1%
2.8343400961
< 0.1%
2.9777300361
< 0.1%
3.2655799391
< 0.1%
3.8924000261
< 0.1%
3.9780700211
< 0.1%
4.1113600731
< 0.1%
4.2242097851
< 0.1%
4.2804098131
< 0.1%
ValueCountFrequency (%)
142.12272641
< 0.1%
133.84614561
< 0.1%
133.33332821
< 0.1%
133.22956851
< 0.1%
133.01501461
< 0.1%
132.15808111
< 0.1%
131.26472471
< 0.1%
130.57971191
< 0.1%
127.97927091
< 0.1%
126.76168061
< 0.1%

parliment seats
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct1212
Distinct (%)26.8%
Missing8887
Missing (%)66.3%
Infinite0
Infinite (%)0.0%
Mean17.45537751
Minimum0
Maximum63.75
Zeros181
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:46.584993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.204819277
Q18.849557522
median15.5
Q324.52217815
95-th percentile39.3442623
Maximum63.75
Range63.75
Interquartile range (IQR)15.67262062

Descriptive statistics

Standard deviation11.58191808
Coefficient of variation (CV)0.6635157604
Kurtosis0.184939012
Mean17.45537751
Median Absolute Deviation (MAD)7.396869245
Skewness0.736857939
Sum78880.85098
Variance134.1408265
MonotonicityNot monotonic
2022-05-18T01:20:46.722865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0181
 
1.4%
16.6666666758
 
0.4%
11.1111111154
 
0.4%
2046
 
0.3%
1036
 
0.3%
13.3333333335
 
0.3%
12.535
 
0.3%
2535
 
0.3%
1235
 
0.3%
2234
 
0.3%
Other values (1202)3970
29.6%
(Missing)8887
66.3%
ValueCountFrequency (%)
0181
1.4%
0.332225913614
 
0.1%
0.61538461545
 
< 0.1%
0.66445182723
 
< 0.1%
0.66889632112
 
< 0.1%
0.91743119279
 
0.1%
1.1764705884
 
< 0.1%
1.190476194
 
< 0.1%
1.2048192776
 
< 0.1%
1.2345679012
 
< 0.1%
ValueCountFrequency (%)
63.754
< 0.1%
61.255
< 0.1%
56.255
< 0.1%
53.412969281
 
< 0.1%
53.22314053
 
< 0.1%
53.076923086
< 0.1%
50.549450551
 
< 0.1%
508
0.1%
49.166666671
 
< 0.1%
48.856209155
< 0.1%

unpaid domestic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct147
Distinct (%)83.1%
Missing13229
Missing (%)98.7%
Infinite0
Infinite (%)0.0%
Mean17.85448271
Minimum5.02051
Maximum31.04081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:46.864560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.02051
5-th percentile11.788292
Q115.41667
median17.29571
Q320.05833
95-th percentile25.249998
Maximum31.04081
Range26.0203
Interquartile range (IQR)4.64166

Descriptive statistics

Standard deviation4.094182139
Coefficient of variation (CV)0.2293083594
Kurtosis1.396815851
Mean17.85448271
Median Absolute Deviation (MAD)2.15682
Skewness0.3623740887
Sum3160.24344
Variance16.76232739
MonotonicityNot monotonic
2022-05-18T01:20:47.011512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.6254
 
< 0.1%
20.833333
 
< 0.1%
20.555563
 
< 0.1%
14.583333
 
< 0.1%
16.253
 
< 0.1%
18.958333
 
< 0.1%
153
 
< 0.1%
15.555563
 
< 0.1%
19.791672
 
< 0.1%
17.638892
 
< 0.1%
Other values (137)148
 
1.1%
(Missing)13229
98.7%
ValueCountFrequency (%)
5.020511
< 0.1%
6.292341
< 0.1%
8.194441
< 0.1%
8.680561
< 0.1%
101
< 0.1%
10.416672
< 0.1%
10.833331
< 0.1%
11.608141
< 0.1%
11.833331
< 0.1%
12.51
< 0.1%
ValueCountFrequency (%)
31.040811
< 0.1%
29.521991
< 0.1%
29.112431
< 0.1%
28.472221
< 0.1%
27.768251
< 0.1%
27.708332
< 0.1%
26.111111
< 0.1%
25.416671
< 0.1%
25.208331
< 0.1%
24.097221
< 0.1%

teenage mothers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct236
Distinct (%)64.3%
Missing13039
Missing (%)97.3%
Infinite0
Infinite (%)0.0%
Mean19.15117166
Minimum1.6
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:47.155580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.4
Q110.15
median18
Q326.45
95-th percentile37.41
Maximum50.6
Range49
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation10.62030546
Coefficient of variation (CV)0.5545512119
Kurtosis-0.6084632729
Mean19.15117166
Median Absolute Deviation (MAD)8.1
Skewness0.4392809322
Sum7028.48
Variance112.790888
MonotonicityNot monotonic
2022-05-18T01:20:47.303889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.54
 
< 0.1%
17.64
 
< 0.1%
13.84
 
< 0.1%
22.84
 
< 0.1%
20.54
 
< 0.1%
4.43
 
< 0.1%
183
 
< 0.1%
23.23
 
< 0.1%
5.93
 
< 0.1%
353
 
< 0.1%
Other values (226)332
 
2.5%
(Missing)13039
97.3%
ValueCountFrequency (%)
1.61
< 0.1%
2.11
< 0.1%
2.81
< 0.1%
2.91
< 0.1%
3.41
< 0.1%
3.51
< 0.1%
3.62
< 0.1%
3.721
< 0.1%
3.81
< 0.1%
4.061
< 0.1%
ValueCountFrequency (%)
50.61
< 0.1%
46.42
< 0.1%
45.41
< 0.1%
43.11
< 0.1%
42.91
< 0.1%
42.51
< 0.1%
41.51
< 0.1%
411
< 0.1%
40.42
< 0.1%
401
< 0.1%

married by 18
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct370
Distinct (%)70.1%
Missing12878
Missing (%)96.1%
Infinite0
Infinite (%)0.0%
Mean27.98243628
Minimum0
Maximum83.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size104.9 KiB
2022-05-18T01:20:47.447637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.8
Q115.3
median25.72021972
Q338.175
95-th percentile60.495
Maximum83.5
Range83.5
Interquartile range (IQR)22.875

Descriptive statistics

Standard deviation16.88516575
Coefficient of variation (CV)0.6034201445
Kurtosis0.04354275703
Mean27.98243628
Median Absolute Deviation (MAD)11.29210088
Skewness0.6417974149
Sum14774.72636
Variance285.1088223
MonotonicityNot monotonic
2022-05-18T01:20:47.585187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.55
 
< 0.1%
315
 
< 0.1%
234
 
< 0.1%
224
 
< 0.1%
34.44
 
< 0.1%
41.24
 
< 0.1%
0.14
 
< 0.1%
23.33
 
< 0.1%
24.63
 
< 0.1%
21.23
 
< 0.1%
Other values (360)489
 
3.6%
(Missing)12878
96.1%
ValueCountFrequency (%)
02
< 0.1%
0.14
< 0.1%
0.21
 
< 0.1%
1.485925581
 
< 0.1%
1.61
 
< 0.1%
1.81
 
< 0.1%
2.21
 
< 0.1%
2.51
 
< 0.1%
3.23
< 0.1%
3.4962911
 
< 0.1%
ValueCountFrequency (%)
83.51
< 0.1%
78.11
< 0.1%
76.61
< 0.1%
76.31
< 0.1%
74.51
< 0.1%
73.31
< 0.1%
721
< 0.1%
71.41
< 0.1%
70.61
< 0.1%
69.91
< 0.1%

Interactions

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2022-05-18T01:20:23.153958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:25.184010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:27.104479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:29.162023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:31.519079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:33.434971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:35.372696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:37.259077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:38.998064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:41.013335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:10.790019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:13.064547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:15.101553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:17.016008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:19.349524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:21.267165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:23.284278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:25.307959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:27.230157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:29.282561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:31.634622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:33.557250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:35.492755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:37.358998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:39.107593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:41.119734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:10.942090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:13.194810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:15.213471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:17.132842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:19.457078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:21.366056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:23.390917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:25.416956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:27.345218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:29.384121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:31.734557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:33.662012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:35.597459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:37.458804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:39.212437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:41.233139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:11.136717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:13.307631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:15.319703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:17.232046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:19.567311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:21.461405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:23.504701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:25.520760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:27.459858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:29.925151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:31.849423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:33.769221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:35.761014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:37.560686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-18T01:20:39.319643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-18T01:20:47.723658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-18T01:20:47.996535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-18T01:20:48.216680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-18T01:20:48.448633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-18T01:20:41.456664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-18T01:20:41.768421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-18T01:20:42.081030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-18T01:20:42.385568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Indicator NameCountry NameYearCPI human resourcesCPIA gender equalityCPIA social protectionemployersmanagementfertility rateintentional homicideslabor forceliteracy ratepoverty gapprimary completionparliment seatsunpaid domesticteenage mothersmarried by 18
0Afghanistan1960NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
1Afghanistan1961NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
2Afghanistan1962NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
3Afghanistan1963NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
4Afghanistan1964NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
5Afghanistan1965NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
6Afghanistan1966NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
7Afghanistan1967NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
8Afghanistan1968NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN
9Afghanistan1969NaNNaNNaNNaNNaN7.45NaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

Indicator NameCountry NameYearCPI human resourcesCPIA gender equalityCPIA social protectionemployersmanagementfertility rateintentional homicideslabor forceliteracy ratepoverty gapprimary completionparliment seatsunpaid domesticteenage mothersmarried by 18
13396Zimbabwe20122.03.02.00.32NaN4.058NaN78.475998NaNNaN97.96295914.953271NaNNaNNaN
13397Zimbabwe20132.53.02.00.32NaN4.030NaN79.411003NaNNaN98.85495031.481481NaNNaNNaN
13398Zimbabwe20143.54.02.50.30NaN3.974NaN80.31400388.283829NaN97.41146931.481481NaNNaN33.500000
13399Zimbabwe20153.54.02.50.31NaN3.896NaN80.299004NaNNaN100.91259831.481481NaN21.632.400000
13400Zimbabwe20163.54.02.50.31NaN3.804NaN80.279999NaNNaN99.20713831.481481NaNNaNNaN
13401Zimbabwe20174.04.02.50.29NaN3.707NaN80.285004NaN45.296.01495432.575758NaNNaNNaN
13402Zimbabwe20184.04.03.00.28NaN3.615NaN80.308998NaNNaN93.23854131.481481NaNNaNNaN
13403Zimbabwe20194.04.03.00.2528.073.531NaN80.338997NaN48.489.32037431.851852NaNNaN33.658057
13404Zimbabwe20204.04.03.0NaNNaN3.460NaN78.980003NaNNaN90.90255031.851852NaNNaNNaN
13405Zimbabwe2021NaNNaNNaNNaNNaNNaNNaN79.307999NaNNaNNaN31.851852NaNNaNNaN